Title :
Evolving spiking neural networks for spatio-and spectro-temporal pattern recognition
Author_Institution :
Knowledge Eng. & Discovery Res. Inst. - KEDRI, Auckland Univ. of Technol., Auckland, New Zealand
Abstract :
This paper provides a survey on the evolution of the evolving connectionist systems (ECOS) paradigm, from simple ECOS introduced in 1998 to evolving spiking neural networks (eSNN) and neurogenetic systems. It presents methods for their use for spatio-and spectro temporal pattern recognition. Future directions are highlighted.
Keywords :
genetics; neural net architecture; neurophysiology; pattern recognition; spatiotemporal phenomena; ECOS paradigm; eSNN; evolving connectionist systems paradigm; evolving spiking neural networks; neurogenetic systems; spatio-temporal pattern recognition; spectro-temporal pattern recognition; Adaptation models; Biological system modeling; Brain models; Computational modeling; Data models; Neurons; Computational Neurogenetic Systems (CNGS); Evolving Connectionist Systems (ECOS); Evolving Spiking Neural Networks (eSNN); quantum inspired SNN; spatio-temporal pattern recognition; spectro-temporal pattern recognition;
Conference_Titel :
Intelligent Systems (IS), 2012 6th IEEE International Conference
Conference_Location :
Sofia
Print_ISBN :
978-1-4673-2276-8
DOI :
10.1109/IS.2012.6335110
